Use data lineage with Dataproc Serverless

This document describes how to enable data lineage on Dataproc Serverless for Spark batch workloads and interactive sessions at the project, batch workload, or interactive session level.

Overview

Data lineage is a Dataplex feature that lets you track how data moves through your systems: where it comes from, where it is passed to, and what transformations are applied to it.

Dataproc Serverless for Spark workloads and sessions capture lineage events and publish them to the Dataplex Data Lineage API. Dataproc Serverless for Spark integrates with the Data Lineage API through OpenLineage, using the OpenLineage Spark plugin.

You can access lineage information through Dataplex, using lineage graphs and the Data Lineage API. For more information, see View lineage graphs in Dataplex.

Availability, capabilities, and limitations

Data lineage, which supports BigQuery and Cloud Storage data sources, is available for workloads and sessions that run with Dataproc Serverless for Spark runtime versions 1.1, 1.2, and 2.2, with the following exceptions and limitations:

  • Data lineage is not available for SparkR or Spark streaming workloads or sessions.

Before you begin

  1. On the project selector page in the Google Cloud console, select the project to use for your Dataproc Serverless for Spark workloads or sessions.

    Go to project selector

  2. Enable the Data Lineage API and Dataplex API.

    Enable the APIs

Required roles

To get the permissions that you need to use data lineage in Dataproc Serverless for Spark, ask your administrator to grant you the following IAM roles on the Dataproc cluster VM service account:

For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Enable data lineage at the project level

You can enable data lineage at the project level. When enabled at the project level, all subsequent batch workloads and interactive sessions that you run in the project will have Spark lineage enabled.

How to enable data lineage at the project level

To enable data lineage at the project level, set the following custom project metadata.

Key Value
DATAPROC_LINEAGE_ENABLED true
DATAPROC_CLUSTER_SCOPES https://www.googleapis.com/auth/cloud-platform

You can disable data lineage at the project level by setting the DATAPROC_LINEAGE_ENABLED metadata to false.

Enable data lineage for a Spark batch workload

You can enable data lineage on a batch workload by setting the spark.dataproc.lineage.enabled property to true when you submit the workload.

Batch workload example

This example submits a batch lineage-example.py workload with Spark lineage enabled.

gcloud dataproc batches submit pyspark lineage-example.py \
    --region=REGION \
    --properties=spark.dataproc.lineage.enabled=true

lineage-example.py reads data from a BigQuery table, and then writes the output to a different BigQuery table.

#!/usr/bin/env python

from pyspark.sql import SparkSession
import sys

spark = SparkSession \
  .builder \
  .appName('LINEAGE_BQ_TO_BQ') \
  .getOrCreate()

bucket = lineage-demo
spark.conf.set('temporaryCloudStorageBucket', bucket)

source = sample.source
words = spark.read.format('bigquery') \
  .option('table', source) \
  .load()
words.createOrReplaceTempView('words')

word_count = spark.sql('SELECT word, SUM(word_count) AS word_count FROM words GROUP BY word')

destination = sample.destination
word_count.write.format('bigquery') \
  .option('table', destination) \
  .save()

You can view the lineage graph in the Dataplex UI.

Spark lineage graph

Enable data lineage for a Spark interactive session

You can enable data lineage on a Spark interactive session by setting the spark.dataproc.lineage.enabled property to true when you create the session or session template.

Interactive session example

The following PySpark notebook code configures a Dataproc Serverless interactive session with Spark data lineage enabled running on a Private Google Access VPC regional subnet. It then creates a Spark Connect session that runs a word count query on a public BigQuery Shakespeare dataset, and then writes the output to a BigQuery table.

from dataproc_spark_session.session.spark.connect import DataprocSparkSession
from google.cloud.dataproc_v1 import Session

session = Session()

# Configure the Dataproc Serverless interactive session. Enable Spark data lineage.
project_id = "sample-project-id"
region = "us-central1"
subnet_name = "sample-private-google-access-subnet"
session.environment_config.execution_config.subnetwork_uri = f"projects/{project_id}/regions/{region}/subnetworks/{subnet_name}"
session.runtime_config.properties["spark.dataproc.lineage.enabled"] = "true"
session.runtime_config.version = "2.2"

# Create the Spark Connect session.
spark = (
   DataprocSparkSession.builder
     .appName("LINEAGE_BQ_TO_BQ")
     .dataprocConfig(session)
     .getOrCreate()
)
# Run a wordcount query on the public BigQuery Shakespeare dataset.
source = "bigquery-public-data:samples.shakespeare"
words = spark.read.format("bigquery").option("table", source).load()
words.createOrReplaceTempView('words')
word_count = spark.sql(
           'SELECT word, SUM(word_count) AS word_count FROM words GROUP BY word')

# Output the results to a BigQuery destination table.
destination = sample.destination
word_count.write.format('bigquery') \
  .option('table', destination) \
  .save()

You can view the data lineage graph by clicking the destination table name listed in the navigation pane on BigQuery Explorer page, then selecting the lineage tab on the table details pane.

Spark lineage graph

View lineage in Dataplex

A lineage graph displays relationships between your project resources and the processes that created them. You can view data lineage information in the Google Cloud console or retrieve the information from the Data Lineage API as JSON data.

What's next